This dissertation identifies the requirements and evaluates an architectural framework for an artificial neural network-based system that is capable of fulfilling monitoring and control requirements of future aerospace missions. Incorporated into this framework are a newly developed training algorithm and the concept of cooperative network architectures. The feasibility of such an approach is assessed for its ability to identify faults in low frequency waveforms and to generate subsequent controlling outputs for a single parameter spacecraft system.